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The paper presents the latest stage in the development of a robust multitarget tracker to be deployed in a large multistatic sonobuoy system. The major technical challenges are: the association of multiple measurements from different receivers for every ping; target detection dependence on the source-target-receiver geometry, ping properties, target properties and acoustic propagation conditions;...
A particle filter (PF) has been recently proposed to detect and track colour objects in video. This study presents an adaptation of the PF to track people in surveillance video. Detection is based on automated background modelling rather than a manually generated object colour model. Furthermore, a labelling method is proposed to create tracks of objects through the scene, rather than unconnected...
The paper makes two contributions. First, a new formulation of the PHD filter which distinguishes between persistent and newborn objects is presented. This formulation results in an efficient sequential Monte Carlo (SMC) implementation of the PHD filter, where the placement of newborn object particles is determined by the measurements. The second contribution is a novel method for the state and error...
Early detection and prediction of the size and the peak time of an epidemic outbreak (malicious or natural) is of crucial importance for a timely medical response (quarantine, vaccination, etc). A conventional approach to this problem is based on large scale agent-based computer simulations. This paper proposes an alternative framework formulated in the context of stochastic nonlinear filtering. The...
Previously a particle filter has been proposed to detect colour objects in video [1]. In this work, the particle filter is adapted to track people in surveillance video. Detection is based on automated background modelling rather than a manually-generated object colour model. A labelling method is proposed that tracks objects through the scene rather than detecting them. A methodical comparison between...
The tutorial is an overview of tracking and data fusion for surveillance systems with applications both to defense and civilian systems. It is divided into four parts: Part 1 - Filtering: Covers the topics related to state estimation for stochastic dynamic systems: optimal Bayesian estimator, Kalman filter, nonlinear filters (extended and unscented Kalman filter, Gaussian sum filter, particle filter);...
Given a map of a polygon-shaped search area with obstacles and a group of mobile networked observers equipped with radiation dose counters (such as the Geiger-Muller counter), the data fusion problem is twofold: (1) to establish if any radioactive point sources are present in the area; (2) if present, to determine their number and their parameters (locations and intensities of radiation). The detection/estimation...
Given a designated polygon-shaped search area with obstacles, the problem is to establish if a radioactive material of unknown intensity of radiation is present in the area, and if present, its whereabouts. The detection/estimation part of the problem is solved in the Bayesian framework using a particle filter. The coordinated search by multiple observers is carried out by maximization of the information...
The paper presents an algorithm for detection and a subsequent information gain driven search for an unaccounted point source of relatively low-level gamma radiation. Source detection and parameter estimation are carried out jointly in the Bayesian framework using a particle filter. The observer control vector consists of the next sensor location and the exposure time. During the pre-detection search,...
This paper studies a non-linear filter due to Benes for which the optimum solution is known. The paper compares the estimation performance of Benes filter to those of well-known approximate filters: the Extended Kalman, the statistical linearisation and the particle filtering. The performance of all these four filters are also compared to the Cramer-Rao lower bound. Thus, the Benes filter is a yardstick...
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